PRover: Proof Generation for Interpretable Reasoning over Rules

• URL: http://arxiv.org/abs/2010.02830v1
• Date: Tue, 6 Oct 2020 15:47:53 GMT
• Title: PRover: Proof Generation for Interpretable Reasoning over Rules
• Authors: Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, Mohit Bansal
• Abstract summary: We propose a transformer-based model that answers binary questions over rule-bases and generates the corresponding proofs. Our model learns to predict nodes and edges corresponding to proof graphs in an efficient constrained training paradigm. We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation.
• Score: 81.40404921232192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work by Clark et al. (2020) shows that transformers can act as 'soft theorem provers' by answering questions over explicitly provided knowledge in natural language. In our work, we take a step closer to emulating formal theorem provers, by proposing PROVER, an interpretable transformer-based model that jointly answers binary questions over rule-bases and generates the corresponding proofs. Our model learns to predict nodes and edges corresponding to proof graphs in an efficient constrained training paradigm. During inference, a valid proof, satisfying a set of global constraints is generated. We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation, with strong generalization performance. First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation). Second, when trained on questions requiring lower depths of reasoning, it generalizes significantly better to higher depths (up to 15% improvement). Third, PROVER obtains near perfect QA accuracy of 98% using only 40% of the training data. However, generating proofs for questions requiring higher depths of reasoning becomes challenging, and the accuracy drops to 65% for 'depth 5', indicating significant scope for future work. Our code and models are publicly available at https://github.com/swarnaHub/PRover

Related papers

• Alchemy: Amplifying Theorem-Proving Capability through Symbolic Mutation [71.32761934724867]
  This work proposes Alchemy, a framework for data synthesis that constructs formal theorems through symbolic mutation. For each candidate theorem in Mathlib, we identify all invocable theorems that can be used to rewrite or apply to it. As a result, our method increases the number of theorems in Mathlib by an order of magnitude, from 110k to 6M.
  arXiv  Detail & Related papers  (2024-10-21T08:04:21Z)
• MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data [85.50740598523818]
  MUSTARD is a framework that masters uniform synthesis of theorem and proof data of high quality and diversity. We present a theorem-and-proof benchmark MUSTARDSAUCE with 5,866 valid data points. We perform extensive analysis and demonstrate that MUSTARD generates validated high-quality step-by-step data.
  arXiv  Detail & Related papers  (2024-02-14T05:57:58Z)
• KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering [11.537283115693432]
  We propose a Knowledge Enhancement and Plausibility Ranking approach grounded on the Generate-Then-Rank pipeline architecture. Specifically, we expand questions in terms of Wiktionary commonsense knowledge of keywords, and reformulate them with normalized patterns. We develop an ELECTRA-based answer ranking model, where logistic regression is conducted during training, with the aim of approxing different levels of plausibility.
  arXiv  Detail & Related papers  (2023-05-15T04:58:37Z)
• Faithful Chain-of-Thought Reasoning [51.21714389639417]
  Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of reasoning tasks. We propose Faithful CoT, a reasoning framework involving two stages: Translation and Problem Solving. This guarantees that the reasoning chain provides a faithful explanation of the final answer.
  arXiv  Detail & Related papers  (2023-01-31T03:04:26Z)
• Generating Natural Language Proofs with Verifier-Guided Search [74.9614610172561]
  We present a novel stepwise method NLProofS (Natural Language Proof Search) NLProofS learns to generate relevant steps conditioning on the hypothesis. It achieves state-of-the-art performance on EntailmentBank and RuleTaker.
  arXiv  Detail & Related papers  (2022-05-25T02:22:30Z)
• Interpretable Proof Generation via Iterative Backward Reasoning [37.03964644070573]
  We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA) We handle the limitations of existed works in two folds: 1) enhance the interpretability of reasoning procedures with detailed tracking, by predicting nodes and edges in the proof path iteratively backward from the question; 2) promote the efficiency and accuracy via reasoning on the elaborate representations of nodes and history paths, without any intermediate texts that may introduce external noise during proof generation.
  arXiv  Detail & Related papers  (2022-05-22T02:44:14Z)
• Probabilistic Graph Reasoning for Natural Proof Generation [22.1374469158861]
  We propose PRobr, a novel approach for joint answer prediction and proof generation. PRobr defines a joint probabilistic distribution over all possible proof graphs and answers. Experiments on multiple datasets verify the effectiveness of PRobr.
  arXiv  Detail & Related papers  (2021-07-06T06:34:41Z)
• Logic-Guided Data Augmentation and Regularization for Consistent Question Answering [55.05667583529711]
  This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions. Our method leverages logical and linguistic knowledge to augment labeled training data and then uses a consistency-based regularizer to train the model.
  arXiv  Detail & Related papers  (2020-04-21T17:03:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.